EGGS: Explicitly Granular 3D Gaussian Splatting via Luma-Aware and Volume-Preserving Attribute Factorization

3D Gaussian Splatting has revolutionized the field of novelview synthesis by enabling high-fidelity and real-time rendering. How-ever, its explicit representation requires millions of primitives, result-ing in massive storage overheads that hinder scalable deployment. Toaddress this challenge, we propose Explicitly Granular Gaussian Splat-ting (EGGS), a novel representation and compression framework thatachieves an ultra-compact storage footprint without compromising vi-sual quality or rendering speed. Our method is built upon two key in-novations designed to minimize attribute dimensionality. First, we intro-duce Luma-based DC Representation, which leverages the dominance ofluminance information to compress the 3-dimensional DC component ofSpherical Harmonics into a single explicit dimension. Second, we proposeVolume-Preserving Scale Factorization, which reduces the 3-dimensionalscale attribute to a single explicit dimension while ensuring training sta-bility by maintaining consistent volume. By combining these techniqueswith a lightweight neural field for implicit attribute estimation, EGGSachieves a remarkably compact representation with the smallest storagefootprint among existing methods. Extensive experiments on the Mip-NeRF360, Tanks&Temples, and Deep Blending datasets demonstratethat EGGS significantly outperforms state-of-the-art methods in rate-distortion efficiency, reducing gigabyte-scale scenes to a mere handful ofmegabytes while maintaining high-quality real-time rendering. Code isavailable here.